{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 案例数据背景介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.数据来源：\n",
    "\n",
    "     Dongjin Cho，韩国蔚山国立科技学院城市与环境工程学院\n",
    "\n",
    "     Cheolhee Yoo，韩国蔚山国立科技学院城市与环境工程学院\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.摘要：\n",
    "\n",
    "    本案例所使用的数据集包含了韩国首尔2013至2017年夏天的14个天气预报（NWP）的气象预报数据、2个现场观测数据和5个地理辅助变量。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.数据集信息：\n",
    "\n",
    "    这些数据是为了修正韩国气象局在首尔运行的LDAPS(Local Data Assimilation and Prediction System)模式下次日最高和最低气温预报的偏差。该数据包括2013年至2017年的夏季数据。输入数据主要由LDAPS模型的次日预报数据、当前的现场最高和最低气温以及地理辅助变量组成。此数据有两个输出数据（即第二天的最高和最低气温）；2015年至2017年期间进行了后期验证。\n",
    "    关于LDAPS模式: http://qikan.camscma.cn/jamsweb/cn/article/doi/10.11898/1001-7313.20200103?viewType=HTML\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.数据属性介绍：\n",
    "\n",
    "      1. station - 气象站编号: 1 - 25 \n",
    "      \n",
    "      2. Date - 记录日期: yyyy-mm-dd ('2013-06-30' to '2017-08-30') \n",
    "      \n",
    "      3. Present_Tmax - 记录日期的0-21时最高气温(Â°C): 20 - 37.6 \n",
    "      \n",
    "      4. Present_Tmin - 记录日期的0-21时最低气温(Â°C): 11.3 - 29.9 \n",
    "      \n",
    "      5. LDAPS_RHmin - LDAPSM模式预测的次日最小相对湿度 (%): 19.8 - 98.5 \n",
    "      \n",
    "      6. LDAPS_RHmax - LDAPS模式预测的次日最大相对湿度 (%): 58.9 - 100 \n",
    "      \n",
    "      7. LDAPS_Tmax_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 17.6 - 38.5 \n",
    "      \n",
    "      8. LDAPS_Tmin_lapse - LDAPS模式预测的次日最高气温递减率 (Â°C): 14.3 - 29.6 \n",
    "      \n",
    "      9. LDAPS_WS - LDAPS 模式预测的次日平均风速 (m/s): 2.9 to 21.9 \n",
    "      \n",
    "      10. LDAPS_LH - LDAPS模式预测的次日平均潜热通量 (W/m2): -13.6 - 213.4 \n",
    "      \n",
    "      11. LDAPS_CC1 - LDAPS模式预测的次日第一个6小时平均云量 (0-5 h) (%): 0 - 0.97 \n",
    "      \n",
    "      12. LDAPS_CC2 - LDAPS模式预测的次日第二个6小时平均云量 (0-5 h) (6-11 h) (%): 0 to 0.97 \n",
    "      \n",
    "      13. LDAPS_CC3 - LDAPS模式预测的次日第三个6小时平均云量 (0-5 h) (12-17 h) (%): 0 to 0.98 \n",
    "      \n",
    "      14. LDAPS_CC4 - LDAPS模式预测的次日第四个6小时平均云量 (0-5 h) (18-23 h) (%): 0 to 0.97 \n",
    "      \n",
    "      15. LDAPS_PPT1 - LDAPS模式预测的次日第一个6小时平均降水量 (0-5 h) (%): 0 to 23.7 \n",
    "      \n",
    "      16. LDAPS_PPT2 - LDAPS模式预测的次日第二个6小时平均降水量  (6-11 h) (%): 0 to 21.6 \n",
    "      \n",
    "      17. LDAPS_PPT3 - LDAPS模式预测的次日第三个6小时平均降水量 (12-17 h) (%): 0 to 15.8 \n",
    "      \n",
    "      18. LDAPS_PPT4 - LDAPS模式预测的次日第四个6小时平均降水量  (18-23 h) (%): 0 to 16.7 \n",
    "      \n",
    "      19. lat - 维度 (Â°): 37.456 - 37.645 \n",
    "      \n",
    "      20. lon - 经度 (Â°): 126.826 - 127.135 \n",
    "      \n",
    "      21. DEM - 高程 (m): 12.4 - 212.3 \n",
    "      \n",
    "      22. Slope - 坡度 (Â°): 0.1 - 5.2 \n",
    "      \n",
    "      23. Solar radiation - 太阳辐射量 (wh/m2): 4329.5 to 5992.9 \n",
    "      \n",
    "      24. Next_Tmax - 次日最高气温 (Â°C): 17.4 to 38.9 \n",
    "      \n",
    "      25. Next_Tmin - 次日最低气温 (Â°C): 11.3 to 29.8\n",
    "      \n",
    "\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**5. sklearn中常见的几个线性回归类库：**\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.589506Z",
     "start_time": "2020-06-16T01:21:34.243221Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pandas_profiling as pp \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#全部行都能输出\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:21:36.939352Z",
     "start_time": "2020-06-16T01:21:36.858414Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>Date</th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>Present_Tmin</th>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>...</th>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "      <th>Next_Tmin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>28.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>58.255688</td>\n",
       "      <td>91.116364</td>\n",
       "      <td>28.074101</td>\n",
       "      <td>23.006936</td>\n",
       "      <td>6.818887</td>\n",
       "      <td>69.451805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>126.991</td>\n",
       "      <td>212.3350</td>\n",
       "      <td>2.7850</td>\n",
       "      <td>5992.895996</td>\n",
       "      <td>29.1</td>\n",
       "      <td>21.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>52.263397</td>\n",
       "      <td>90.604721</td>\n",
       "      <td>29.850689</td>\n",
       "      <td>24.035009</td>\n",
       "      <td>5.691890</td>\n",
       "      <td>51.937448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6046</td>\n",
       "      <td>127.032</td>\n",
       "      <td>44.7624</td>\n",
       "      <td>0.5141</td>\n",
       "      <td>5869.312500</td>\n",
       "      <td>30.5</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.6</td>\n",
       "      <td>23.3</td>\n",
       "      <td>48.690479</td>\n",
       "      <td>83.973587</td>\n",
       "      <td>30.091292</td>\n",
       "      <td>24.565633</td>\n",
       "      <td>6.138224</td>\n",
       "      <td>20.573050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5776</td>\n",
       "      <td>127.058</td>\n",
       "      <td>33.3068</td>\n",
       "      <td>0.2661</td>\n",
       "      <td>5863.555664</td>\n",
       "      <td>31.1</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>32.0</td>\n",
       "      <td>23.4</td>\n",
       "      <td>58.239788</td>\n",
       "      <td>96.483688</td>\n",
       "      <td>29.704629</td>\n",
       "      <td>23.326177</td>\n",
       "      <td>5.650050</td>\n",
       "      <td>65.727144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.6450</td>\n",
       "      <td>127.022</td>\n",
       "      <td>45.7160</td>\n",
       "      <td>2.5348</td>\n",
       "      <td>5856.964844</td>\n",
       "      <td>31.7</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>6/30/2013</td>\n",
       "      <td>31.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>56.174095</td>\n",
       "      <td>90.155128</td>\n",
       "      <td>29.113934</td>\n",
       "      <td>23.486480</td>\n",
       "      <td>5.735004</td>\n",
       "      <td>107.965535</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.5507</td>\n",
       "      <td>127.135</td>\n",
       "      <td>35.0380</td>\n",
       "      <td>0.5055</td>\n",
       "      <td>5859.552246</td>\n",
       "      <td>31.2</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   station       Date  Present_Tmax  Present_Tmin  LDAPS_RHmin  LDAPS_RHmax  \\\n",
       "0      1.0  6/30/2013          28.7          21.4    58.255688    91.116364   \n",
       "1      2.0  6/30/2013          31.9          21.6    52.263397    90.604721   \n",
       "2      3.0  6/30/2013          31.6          23.3    48.690479    83.973587   \n",
       "3      4.0  6/30/2013          32.0          23.4    58.239788    96.483688   \n",
       "4      5.0  6/30/2013          31.4          21.9    56.174095    90.155128   \n",
       "\n",
       "   LDAPS_Tmax_lapse  LDAPS_Tmin_lapse  LDAPS_WS    LDAPS_LH  ...  LDAPS_PPT2  \\\n",
       "0         28.074101         23.006936  6.818887   69.451805  ...         0.0   \n",
       "1         29.850689         24.035009  5.691890   51.937448  ...         0.0   \n",
       "2         30.091292         24.565633  6.138224   20.573050  ...         0.0   \n",
       "3         29.704629         23.326177  5.650050   65.727144  ...         0.0   \n",
       "4         29.113934         23.486480  5.735004  107.965535  ...         0.0   \n",
       "\n",
       "   LDAPS_PPT3  LDAPS_PPT4      lat      lon       DEM   Slope  \\\n",
       "0         0.0         0.0  37.6046  126.991  212.3350  2.7850   \n",
       "1         0.0         0.0  37.6046  127.032   44.7624  0.5141   \n",
       "2         0.0         0.0  37.5776  127.058   33.3068  0.2661   \n",
       "3         0.0         0.0  37.6450  127.022   45.7160  2.5348   \n",
       "4         0.0         0.0  37.5507  127.135   35.0380  0.5055   \n",
       "\n",
       "   Solar radiation  Next_Tmax  Next_Tmin  \n",
       "0      5992.895996       29.1       21.2  \n",
       "1      5869.312500       30.5       22.5  \n",
       "2      5863.555664       31.1       23.9  \n",
       "3      5856.964844       31.7       24.3  \n",
       "4      5859.552246       31.2       22.5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = pd.read_csv(r\"Dataset.csv\")\n",
    "Data.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基本统计信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.892284Z",
     "start_time": "2020-06-16T00:12:45.810809Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>station</th>\n",
       "      <td>7750.0</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>7.211568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Present_Tmin</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>23.225059</td>\n",
       "      <td>2.413961</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>29.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmin</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>56.759372</td>\n",
       "      <td>14.668111</td>\n",
       "      <td>19.794666</td>\n",
       "      <td>45.963543</td>\n",
       "      <td>55.039024</td>\n",
       "      <td>67.190056</td>\n",
       "      <td>98.524734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmin_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>23.512589</td>\n",
       "      <td>2.345347</td>\n",
       "      <td>14.272646</td>\n",
       "      <td>22.089739</td>\n",
       "      <td>23.760199</td>\n",
       "      <td>25.152909</td>\n",
       "      <td>29.619342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.485003</td>\n",
       "      <td>1.762807</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>21.621661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.278200</td>\n",
       "      <td>1.161809</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007896</td>\n",
       "      <td>15.841235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmin</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>22.932220</td>\n",
       "      <td>2.487613</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>21.300000</td>\n",
       "      <td>23.100000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>29.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "station           7750.0    13.000000    7.211568     1.000000     7.000000   \n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "Present_Tmin      7682.0    23.225059    2.413961    11.300000    21.700000   \n",
       "LDAPS_RHmin       7677.0    56.759372   14.668111    19.794666    45.963543   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_Tmin_lapse  7677.0    23.512589    2.345347    14.272646    22.089739   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT2        7677.0     0.485003    1.762807     0.000000     0.000000   \n",
       "LDAPS_PPT3        7677.0     0.278200    1.161809     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "Next_Tmin         7725.0    22.932220    2.487613    11.300000    21.300000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "station             13.000000    19.000000    25.000000  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "Present_Tmin        23.400000    24.900000    29.900000  \n",
       "LDAPS_RHmin         55.039024    67.190056    98.524734  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_Tmin_lapse    23.760199    25.152909    29.619342  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT2           0.000000     0.018364    21.621661  \n",
       "LDAPS_PPT3           0.000000     0.007896    15.841235  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  \n",
       "Next_Tmin           23.100000    24.600000    29.800000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.describe().T "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:45.952260Z",
     "start_time": "2020-06-16T00:12:45.895274Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7752 entries, 0 to 7751\n",
      "Data columns (total 25 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   station           7750 non-null   float64\n",
      " 1   Date              7750 non-null   object \n",
      " 2   Present_Tmax      7682 non-null   float64\n",
      " 3   Present_Tmin      7682 non-null   float64\n",
      " 4   LDAPS_RHmin       7677 non-null   float64\n",
      " 5   LDAPS_RHmax       7677 non-null   float64\n",
      " 6   LDAPS_Tmax_lapse  7677 non-null   float64\n",
      " 7   LDAPS_Tmin_lapse  7677 non-null   float64\n",
      " 8   LDAPS_WS          7677 non-null   float64\n",
      " 9   LDAPS_LH          7677 non-null   float64\n",
      " 10  LDAPS_CC1         7677 non-null   float64\n",
      " 11  LDAPS_CC2         7677 non-null   float64\n",
      " 12  LDAPS_CC3         7677 non-null   float64\n",
      " 13  LDAPS_CC4         7677 non-null   float64\n",
      " 14  LDAPS_PPT1        7677 non-null   float64\n",
      " 15  LDAPS_PPT2        7677 non-null   float64\n",
      " 16  LDAPS_PPT3        7677 non-null   float64\n",
      " 17  LDAPS_PPT4        7677 non-null   float64\n",
      " 18  lat               7752 non-null   float64\n",
      " 19  lon               7752 non-null   float64\n",
      " 20  DEM               7752 non-null   float64\n",
      " 21  Slope             7752 non-null   float64\n",
      " 22  Solar radiation   7752 non-null   float64\n",
      " 23  Next_Tmax         7725 non-null   float64\n",
      " 24  Next_Tmin         7725 non-null   float64\n",
      "dtypes: float64(24), object(1)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "Data.info() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.101859Z",
     "start_time": "2020-06-16T00:12:45.955239Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station              2\n",
       "Date                 2\n",
       "Present_Tmax        70\n",
       "Present_Tmin        70\n",
       "LDAPS_RHmin         75\n",
       "LDAPS_RHmax         75\n",
       "LDAPS_Tmax_lapse    75\n",
       "LDAPS_Tmin_lapse    75\n",
       "LDAPS_WS            75\n",
       "LDAPS_LH            75\n",
       "LDAPS_CC1           75\n",
       "LDAPS_CC2           75\n",
       "LDAPS_CC3           75\n",
       "LDAPS_CC4           75\n",
       "LDAPS_PPT1          75\n",
       "LDAPS_PPT2          75\n",
       "LDAPS_PPT3          75\n",
       "LDAPS_PPT4          75\n",
       "lat                  0\n",
       "lon                  0\n",
       "DEM                  0\n",
       "Slope                0\n",
       "Solar radiation      0\n",
       "Next_Tmax           27\n",
       "Next_Tmin           27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data.isna().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:46.301511Z",
     "start_time": "2020-06-16T00:12:46.110848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 25)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取出所有有空值的记录  （5分）\n",
    "for i in Data.columns:\n",
    "    all_null_record=Data[Data[i].isna()]  \n",
    "all_null_record.shape # 27行有空缺值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看每列数据的分布状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:12:55.448321Z",
     "start_time": "2020-06-16T00:12:46.308487Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa091bae9a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa095df70d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa096219610>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0961c53a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09afb2610>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09b2c0c40>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09af73df0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09af95eb0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa095dfbc70>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0960ab0a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09b4a0940>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09b7aadf0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09b859c70>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09b8947c0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09bb7d4c0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09bc71af0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09be605b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09bf4e3d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ixD88HssTn+3me0+vpMHj4aapmcyemK7wboW48CBunJzBOxv3s6e0+YE1f1q8i5BAFzdNzfTJ9nvHhDI0JYrX1xb4ZH0tUXWsnhdX5fHUsj18uaechIhgRqVFc+6gXtQ1ePjFvzYz+eElvPDlXqxGi3YoXxyBn2+tLfPBesSPKo/Wc9/C9Xy6o5TLR6YwNj22Xf2xPdmt5/Rj/pd7efzTHP7wvVGnXe6zHSW8n13EvRcM9OkvyVnjUvnVe1vZcaDK7wN7thUd5o11BdQ1eJgxLJmJfeO++rmZMykday0bCyr546Kd/OLtLazPq+Chq0f4fY4aaaJrkXqAfeU1XPPEcr7YXcavZw7jr7PHKLzbISEymOsnZfCvDYXsLK465TJH6xr5xdub6ZcQzg/P7+/T7c8c3Ru3y/DGOv8ehWcXVLBg1T5iwgK5+/wBnDso4Rs/N8YYRqfF8I+bJvCjiwbx1oZCZv99JUfqGvxamzRpb4Bb4GNjzFpjzO2nWsAYc7sxJssYk1VaWtrOzUlrrdpTzlWPL6e8po4XbpnEjVMye9zVI/5w53n9iQkN5PZ/Zp1ydOSfl+wi/+BRHr56BMFu3/6yjI8IZvqQRN5cV+i3aWazCyp4NSuftLgwbju7H4lRIWdc3uUy3HPBQJ68fizZBRXc8/IGTb7VAdob4NOstWOBS4G7jDHnnryAtXaetXa8tXZ8QkLL53/oTqy17Cuv4bWsfN7L3s9HWw6wbGcpu4qr/HrX89ey8rnh2VXEhgfxrx9OY3K/eL9tq6dJiAzm6RvHsb/iGD9csI56b5B6PJa/LNnF08ty+N74VL/9m88al0pZdS3Ldvn+oGhFTtlX4X3T1EyCW/HX2ozhKTxw5TAWbyvmvez96hP3s3b1gVtr93u/lxhj3gImAst8UVh3sSKnjEc/2M7Ggqa7qgQGmK+GmQOEBLoYlhLNOQN7NXuU01Iea3nkg+08tTSHaQPieWLOOKLDdIWAr43PjOM314zgx69tZObfljO5Xzx7yqr5bEcpV4/pw4NXDvfbts8fkkh8eJDPrwkvrDjK3S+tJz4imLlTMtv018P3p2RScOgo85btIS02jDF+uN2dNGlzgBtjwgGXtbbK+/hi4Fc+q8zhyqtr+enr2SzZXkLv6BB+ecVQpg3oxercgxigtsFDblkNW/ZXkl1Ywbq8QwzvE82FrRzhd7Ka2gZeycpnd0k1cyal8+CVwwjUsGu/+c64VI7WN/Kv9YW8tHofjR7Lr2cO44bJGX7tqgoMcDFzdB9eXLnPZ9eEH6tv5M4X1lLf4GHuOf3adZ7kZzOG8OHmA7ybvZ9+CRFEh+oAwh/acwSeBLzl/SF1Ay9Zaz/0SVUOt63oMLf9M4uSqlruv3QIN03N/Oo/Q9bepsmIQgIDOCslirNSopgxPIXlu8v4ck85W/ZXUl5T26YrF/aUVfP62gKqjjXwyDUjuG5i950Doiu5YXIGN0zOoKHRQ22Dh3AfXO/dErPGpfLc8lyfXRP+q/e2sqmwkr9/fzylVbXtWleAy/Ddcan85ZNdvLmugJum6tyLP7T50Mxau8daO8r7Ncxa+5AvC3OqpTtL+c6TK6hv9PDaHVO487z+zR7JRAS7uWRYMj+5eDATMuNYsCqPc3/7Kb/7aDuHapqfPvRIbQNvrivgmc9zcRnDHef2U3h3AneAq8PCG5rmCR+aEsXC1fnt7mv+cPMBXlqVxx3n9uOiob7pkomPCObS4SnsKqlmzV7NougPGonpQzml1Tz47hb6J0Tw/M0TSGpln3ZEsJuZo/vw0NXDeWzxLp74LIfnl+9lxrBkrhjVmwl9474azVfX4KGo8ihr9x1iY0EFjR7LuQMTmD4kkSC3ukx6ihunZPDzNzexIqecaQN6tWkdByqPcf+b2QzvE9WmqXLPZFLfODYVVvLRlgMM7x1FWAf+gusJ9K/pI/vKa3jhy31k9grjxVsnEdeOPskBiZH8bc5Y7imu4tnPc/lgcxFvri8EIDLEjQEOH2u6zjYwoOk63Kn9e7X6F4Y439Vj+vDHRTu9J6xbH+Aej+XHr22gtt7Dn68b4/Nf/sYYrhjZm79+sotF24rbdUcj+SYFuA+UVdUy/8u9RIW62x3eJxqUFMmjs0by66uGs3x3GTuKqyiqOMqmwkriwpumdh2QEKFRbz1YSGAAP5jWl0c/3M7mwspW33Lu75/vYfnucn5zzQj6J7Rscq7WSo4OYVK/OFbtOcjEvnH/MWGatI/+1m6no3WN/HPlXgKM4eapfUmM9P1RcJDbxflDErnzvP48OHM4s8alMX1IIiP6RCu8hesnpxMZ7OappTmt+tymgkp+//EOLhmWxHUT0vxUXZMLz0oiJDCA97KLdG24DynA26HRY3l5dR6Hauq5flKGpveUThEVEsicyen8e1MRu0tOPbT/ZDW1Ddy7cD3x4cE8cs1Iv18hEhbk5sKhSeSW1bD9QMtqlOYpwNvh4y0H2F1azVVj+pDZK7yzy5Ee7Naz+xEVGshPXstudnh9o8dy78IN7C2v4Y/fG9VhBx4TM+OIDw/ioy0HaPToKNwXFOBttGV/JZ/vLmNS3zjGZWikmXSuhMhgfj1zOBvyK3h62Z4zLvvwv7exeFsx/+/yoUxt45UrbRHgMlwyLJmSqlrW5+myQl9QgLdBeXUtr68tIDU2lG+PSOnsckQAuGJUby4fmcJji3eyZX/lN9631vLU0hye/SKXm6ZmctO0vh1e47DeUaTFhrJ4WzF1Df6bB6inUIC3UkOjh5dX5+EyhtkT03V3cOlSfj1zOLFhQVz79EpeXfP1AJ+DNXXc9s+1PPLBdi4dnswvLh/aKfUZY5gxPIXDxxpYkaPbCLSXLiNspY+3FrO/8hg3TMogNkwnLaVriQ0P4o3/msr/en0jP30jm+dXNN0lp+DQ0aa751w+lJunZuLqxPud9u0VzlnJkSzdWUp5da3uCNUOOnxshZ3FVXzh7fce2juqs8sROaW0uDBeunUyD1wxlIjgAFJjw7hiVG/eumsqt5zdt1PD+7hLhiVT1+Dhr5/s7uxSHE1H4C1U5u33TooK5jL1e0sX53IZbprWt1P6uVsiMSqE8ZmxLFi1jx9M60t6fFhnl+RIOgJvAY/H8pPXNnKsvpFrJ6RrelYRH7hgSBIBLsOjH23v7FIcS0nUAs+v2MtnO0q5bEQKyZpvRMQnokIDufO8/ryfXaQTmm2kAG/G5sJKHv1gOxeelcSkvnGdXY5It3Lnef1Jjwvj/7295avb0knLKcDP4EhdA/csXE9seCC/neX/4cYiPU1IYAAPXDmU3SXVPL88t7PLcRwF+Bn86t2t5JbV8KfvjfbZDIMi8p+mD0niwrOSeGzxLvIPHunschxFAX4a/95UxMI1+dx5Xv8OHW4s0hM9cOVQAozhnoXr1ZXSCgrwU8grP8LP3shmVFoMP7poUGeXI9LtpcaG8fA1I1ifV8GfFu3s7HIcQwF+ktqGRu56aR0G+NvsMbpkUKSDXDGqN9dNSOPJpTl8uqOks8txBKXTSR5+fxubCiv53XdHkRanwQUiHemXVwxjcFIkd76wlmU7S9u9Po+13foGEhqJeYL3svcz/8t93Hp2Xy4ZltzZ5Yj0OKFBASy4dRLXP7OKW/+ZxVM3jGX6kKQWfba2vpHdpdXsOFBFTmk1R+oaqW3w8NsPtzO0dxSjUmO4dkIa/fx067jOoAD3yi2r4f43NjEmPYafXTqks8sR6bHiI4J5+bbJ3PjcKn7wjyyuGdOHn1wymN4x37yX5t6ymqb7xR6oIreshkZrCXa7GJAYQUxoIMGBAaREh7Bl/2GeW57L08v2MH1IIj+6aFCr7x/aFSnAgWP1jdy1YB3uAMPf5oxVv7dIJ4sND2Lh7VN44tPdPPNFLu9vKmJkajRDkqMIdrsoOHSUHcVNoQ2QEBHM1P7xDE6OJCM+nIATJuyaMykdgNKqWl5cuY8XVu5j5uPLue2cftx34UBCAp17X9keH+DWWn7xr81sLTrMczeNp88pfsuLSMeLCHbz0xlDmDMpnWc+z2VzYSX/Wl9IvcdDn5hQ+vUKZ+6UDKprG1s0TiMhMpj/uWgQPzi7Lw+9v5WnluawZFsx874/nr4OvSVijw/wecv28NraAu6ZPqDFfW0i0nFSY8N44MphAF+dkDxxVPRLq/Jatb7o0EB+O2sUl4/szb0L1zPzb1/w+PVjOWdggu+K7iA9uq/goy0HeOTD7Xx7ZAr3XajrvUW6OmOMz6a0OHdQAu/cfTYp0aHc9Pwanl+e67grVnpsgK/OPci9C9czMjWGP3x3VJeY5F5EOlZaXBhv/HAq04ck8uC7W7n/jU2Ouldnjwzw9XmHuPn51fSJCeXZueMdfRJDRNonItjN0zeM4+7zB/BKVj7XP7OS8urazi6rRXpcgGcXVDD3udXERwSz4NbJ9NL9+ER6PJfL8JNLBvOX2WPILqhk5uPL2XGgqrPLalaPCvAl24q59umVRIYEsuDWSSRH6+YMIvK1K0f15pU7plDX4OGaJ5bzzsb9nV3SGfWIALfW8o/ludz2zywGJEbw1l1TNUxeRE5pdFoMb989jcHJkdzz8nr+z1ubOFbf2NllnVK3D/DSqlpumZ/FA+9uZfqQRBbePpnESB15i8jppUSH8sodU7jj3H4sWJXHZX/+nJV7yju7rG/otgFe3+jhxZX7mPHYMr7YXcYvrxjKvBvHEx7c4y99F5EWCAxw8fPLzuKFWyZS7/Fw3byV/PjVjV3qphPdLs1qaht4P7uIJ5fmkFtWw4TMWB66egSDkiI7uzQRcaBzBibw8X3n8diSnTy/fC9vbyjkO2NTuXFKBsN6R3XqrRbbFeDGmBnAn4EA4Blr7SM+qaqVDtbUsSKnjKU7Svlg8wGqaxsYkhzJs3PHM31Iou5lKSLtEhoUwM8vPYubp/blqaU5vLQ6j1ey8hmSHMllI1KYNiCekakxHT6PUpsD3BgTADwOXAQUAGuMMe9Ya7f6qrjj8sqPUFhxlCN1DVTXNlBaVUvx4WPklh1hR/Fh8g8eBSAy2M2M4cnMnpjG2PRYBbeI+FRydAgPXDmM+y4cyLvZRbyxtoA/LtrJHxdBSGDTLIiDEiNJjQ0lMSqE+PAgQoMCCA92MygxkuiwQJ/W054j8InAbmvtHgBjzEJgJuDzAJ/3eQ4vrvzP+Q6C3S7S4sIYmRrDdRPSmdwvnlGp0bg1k6CI+FlMWBA3Ts7gxskZHKypY9WectbsPcSukipW5JRTUnUMz0mj8p+/eQLnD070aR2mrWP/jTGzgBnW2lu9z28EJllr7z5puduB271PBwM72l5uh+gFlHV2EZ1Ebe+ZemrbndTuDGvtN2bbas8R+Kn6J77x28BaOw+Y147tdChjTJa1dnxn19EZ1Ha1vSfpDu1uT39DAZB2wvNUoGsPWxIR6UbaE+BrgIHGmL7GmCDgOuAd35QlIiLNaXMXirW2wRhzN/ARTZcRPmet3eKzyjqPY7p7/EBt75l6atsd3+42n8QUEZHOpWvuREQcSgEuIuJQ3TbAjTEhxpjVxpiNxpgtxpgHva+/YozZ4P3aa4zZcIZ1BBhj1htj3jvhtQeMMYUnrOOyjmhPa7S37d73NnmXyzrh9ThjzCJjzC7v99iOalNL+bHtPWG/xxhjXjfGbDfGbDPGTPG+3hP2++na3qX3e7ebzOoEtcB0a221MSYQ+MIY84G19trjCxhj/gBUnmEd9wLbgKiTXv+Ttfb3Pq/Yd3zR9vOttScPcrgfWGKtfcQYc7/3+c98XXw7+avt0P33+5+BD621s7xXlh2fNL8n7PfTtR268H7vtkfgtkm192mg9+urM7bGGAN8D3j5VJ83xqQC3wae8XOpPtfetp/BTGC+9/F84Kp2lupzfmx7l9eethtjooBzgWe966qz1lZ43+7W+72Ztndp3TbA4asukA1ACbDIWrvqhLfPAYqttbtO8/HHgJ8Cp7pF9d3GmGxjzHNd8c9JaHfbLfCxMWataZoK4bgka20RgPe7byd28BE/tR26937vB5QCz5umbsNnjDHh3ve6+34/U9uhC+/3bh3g1tpGa+1omkaJTjTGDD/h7dmc/uj7cqDEWrv2FMTjUJoAAAKYSURBVG8/CfQHRgNFwB98W7VvtLXtXtOstWOBS4G7jDHn+rFUn/NT27v7fncDY4EnrbVjgBqaukocw09t79L7vVsH+HHeP4c+A2YAGGPcwDXAK6f5yDTgSmPMXmAhMN0Y86J3XcXeHxQP8HeaZmXsstrQdqy1+73fS4C3+LqNxcaYFO96Umg60umyfNn2HrDfC4CCE45aX6cp1KD77/fTtr2r7/duG+DGmARjTIz3cShwIbDd+/aFwHZrbcGpPmut/bm1NtVam0nTFAGfWGtv8K4r5YRFrwY2+6kJbdaethtjwo0xkccfAxfzdRvfAeZ6H88F3vZPC9rOX23v7vvdWnsAyDfGDPa+dAFfTw3drff7mdre1fd7d74KJQWYb5puPOECXrXWHr8c8DpO+nPKGNObprsKNXeZ0G+NMaNp6ivdC9zh06p9oz1tTwLeajrngxt4yVr7oXfRR4BXjTG3AHnAd/3ektbzV9u7+34H+G9ggfcqjD3Azd7Xu/t+h9O3vUvvdw2lFxFxqG7bhSIi0t0pwEVEHEoBLiLiUApwERGHUoCLiDiUAlx6HGNMdTPvxxhjfthR9Yi0lQJc5JtiAAW4dHkKcOmxjDERxpglxph1pmkO8Jnetx4B+pum+Z9/15k1ipyJBvJIj2OMqbbWRnjnyAiz1h42xvQCVgIDgQzgPWvt8DOuSKSTdeeh9CLNMcDD3hkHPUAfmobTiziCAlx6suuBBGCctbbeO/tkSOeWJNJy6gOXniyapnnf640x59PUdQJQBUR2XlkiLaMAl55sATDeNN28+Hq8049aa8uB5caYzTqJKV2ZTmKKiDiUjsBFRBxKAS4i4lAKcBERh1KAi4g4lAJcRMShFOAiIg6lABcRcaj/Dz3dUObsqFPwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09c18f640>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09c5888e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09c676cd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09c676d30>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09bb80cd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa09b4d61f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制每列数据的直方图（分布频次）\n",
    "plot = Data.drop('Date',axis=1) # 删除'Date'列\n",
    "\n",
    "for I in plot.columns:\n",
    "    sns.distplot(plot[I]) \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 生成数据概况报告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:21.940528Z",
     "start_time": "2020-06-16T00:12:55.451319Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec19b9c53f1d49319ed8c3ed1cf1f105",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/38 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生成整体数据概要\n",
    "data_report=pp.ProfileReport(Data) \n",
    "data_report\n",
    "\n",
    "# 也可以把生成的报告导出保存\n",
    "data_report.to_file('report.html') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:52.629282Z",
     "start_time": "2020-06-16T01:33:52.621285Z"
    }
   },
   "outputs": [],
   "source": [
    "# 由于本数据集的特殊性（有两个样本标签：次日最高气温和最低气温）\n",
    "#，本案例中我们只进行次日最高温预测--因此这里只选出与最高温预测相关的变量，作为建模要用的数据\n",
    "\n",
    "Max = Data[['Present_Tmax','LDAPS_RHmax','LDAPS_Tmax_lapse','LDAPS_WS','LDAPS_LH','LDAPS_CC1','LDAPS_CC2','LDAPS_CC3','LDAPS_CC4','LDAPS_PPT1','LDAPS_PPT4',\n",
    "          'lat','lon','DEM','Slope','Solar radiation','Next_Tmax']] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:33:56.575804Z",
     "start_time": "2020-06-16T01:33:56.551818Z"
    }
   },
   "outputs": [],
   "source": [
    "Max.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.290321Z",
     "start_time": "2020-06-16T00:15:22.130413Z"
    }
   },
   "outputs": [],
   "source": [
    "Max.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:22.570160Z",
     "start_time": "2020-06-16T00:15:22.293320Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Present_Tmax</th>\n",
       "      <td>7682.0</td>\n",
       "      <td>29.768211</td>\n",
       "      <td>2.969999</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>88.374804</td>\n",
       "      <td>7.192004</td>\n",
       "      <td>58.936283</td>\n",
       "      <td>84.222862</td>\n",
       "      <td>89.793480</td>\n",
       "      <td>93.743629</td>\n",
       "      <td>100.000153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>29.613447</td>\n",
       "      <td>2.947191</td>\n",
       "      <td>17.624954</td>\n",
       "      <td>27.673499</td>\n",
       "      <td>29.703426</td>\n",
       "      <td>31.710450</td>\n",
       "      <td>38.542255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>7.097875</td>\n",
       "      <td>2.183836</td>\n",
       "      <td>2.882580</td>\n",
       "      <td>5.678705</td>\n",
       "      <td>6.547470</td>\n",
       "      <td>8.032276</td>\n",
       "      <td>21.857621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>62.505019</td>\n",
       "      <td>33.730589</td>\n",
       "      <td>-13.603212</td>\n",
       "      <td>37.266753</td>\n",
       "      <td>56.865482</td>\n",
       "      <td>84.223616</td>\n",
       "      <td>213.414006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.368774</td>\n",
       "      <td>0.262458</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146654</td>\n",
       "      <td>0.315697</td>\n",
       "      <td>0.575489</td>\n",
       "      <td>0.967277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.356080</td>\n",
       "      <td>0.258061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140615</td>\n",
       "      <td>0.312421</td>\n",
       "      <td>0.558694</td>\n",
       "      <td>0.968353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.318404</td>\n",
       "      <td>0.250362</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.101388</td>\n",
       "      <td>0.262555</td>\n",
       "      <td>0.496703</td>\n",
       "      <td>0.983789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.299191</td>\n",
       "      <td>0.254348</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.081532</td>\n",
       "      <td>0.227664</td>\n",
       "      <td>0.499489</td>\n",
       "      <td>0.974710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.591995</td>\n",
       "      <td>1.945768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.052525</td>\n",
       "      <td>23.701544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <td>7677.0</td>\n",
       "      <td>0.269407</td>\n",
       "      <td>1.206214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>16.655469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lat</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>37.544722</td>\n",
       "      <td>0.050352</td>\n",
       "      <td>37.456200</td>\n",
       "      <td>37.510200</td>\n",
       "      <td>37.550700</td>\n",
       "      <td>37.577600</td>\n",
       "      <td>37.645000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lon</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>126.991397</td>\n",
       "      <td>0.079435</td>\n",
       "      <td>126.826000</td>\n",
       "      <td>126.937000</td>\n",
       "      <td>126.995000</td>\n",
       "      <td>127.042000</td>\n",
       "      <td>127.135000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DEM</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>61.867972</td>\n",
       "      <td>54.279780</td>\n",
       "      <td>12.370000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>45.716000</td>\n",
       "      <td>59.832400</td>\n",
       "      <td>212.335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slope</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>1.257048</td>\n",
       "      <td>1.370444</td>\n",
       "      <td>0.098475</td>\n",
       "      <td>0.271300</td>\n",
       "      <td>0.618000</td>\n",
       "      <td>1.767800</td>\n",
       "      <td>5.178230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Solar radiation</th>\n",
       "      <td>7752.0</td>\n",
       "      <td>5341.502803</td>\n",
       "      <td>429.158867</td>\n",
       "      <td>4329.520508</td>\n",
       "      <td>4999.018555</td>\n",
       "      <td>5436.345215</td>\n",
       "      <td>5728.316406</td>\n",
       "      <td>5992.895996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Next_Tmax</th>\n",
       "      <td>7725.0</td>\n",
       "      <td>30.274887</td>\n",
       "      <td>3.128010</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>28.200000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.600000</td>\n",
       "      <td>38.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean         std          min          25%  \\\n",
       "Present_Tmax      7682.0    29.768211    2.969999    20.000000    27.800000   \n",
       "LDAPS_RHmax       7677.0    88.374804    7.192004    58.936283    84.222862   \n",
       "LDAPS_Tmax_lapse  7677.0    29.613447    2.947191    17.624954    27.673499   \n",
       "LDAPS_WS          7677.0     7.097875    2.183836     2.882580     5.678705   \n",
       "LDAPS_LH          7677.0    62.505019   33.730589   -13.603212    37.266753   \n",
       "LDAPS_CC1         7677.0     0.368774    0.262458     0.000000     0.146654   \n",
       "LDAPS_CC2         7677.0     0.356080    0.258061     0.000000     0.140615   \n",
       "LDAPS_CC3         7677.0     0.318404    0.250362     0.000000     0.101388   \n",
       "LDAPS_CC4         7677.0     0.299191    0.254348     0.000000     0.081532   \n",
       "LDAPS_PPT1        7677.0     0.591995    1.945768     0.000000     0.000000   \n",
       "LDAPS_PPT4        7677.0     0.269407    1.206214     0.000000     0.000000   \n",
       "lat               7752.0    37.544722    0.050352    37.456200    37.510200   \n",
       "lon               7752.0   126.991397    0.079435   126.826000   126.937000   \n",
       "DEM               7752.0    61.867972   54.279780    12.370000    28.700000   \n",
       "Slope             7752.0     1.257048    1.370444     0.098475     0.271300   \n",
       "Solar radiation   7752.0  5341.502803  429.158867  4329.520508  4999.018555   \n",
       "Next_Tmax         7725.0    30.274887    3.128010    17.400000    28.200000   \n",
       "\n",
       "                          50%          75%          max  \n",
       "Present_Tmax        29.900000    32.000000    37.600000  \n",
       "LDAPS_RHmax         89.793480    93.743629   100.000153  \n",
       "LDAPS_Tmax_lapse    29.703426    31.710450    38.542255  \n",
       "LDAPS_WS             6.547470     8.032276    21.857621  \n",
       "LDAPS_LH            56.865482    84.223616   213.414006  \n",
       "LDAPS_CC1            0.315697     0.575489     0.967277  \n",
       "LDAPS_CC2            0.312421     0.558694     0.968353  \n",
       "LDAPS_CC3            0.262555     0.496703     0.983789  \n",
       "LDAPS_CC4            0.227664     0.499489     0.974710  \n",
       "LDAPS_PPT1           0.000000     0.052525    23.701544  \n",
       "LDAPS_PPT4           0.000000     0.000041    16.655469  \n",
       "lat                 37.550700    37.577600    37.645000  \n",
       "lon                126.995000   127.042000   127.135000  \n",
       "DEM                 45.716000    59.832400   212.335000  \n",
       "Slope                0.618000     1.767800     5.178230  \n",
       "Solar radiation   5436.345215  5728.316406  5992.895996  \n",
       "Next_Tmax           30.500000    32.600000    38.900000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 填充缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:07.620687Z",
     "start_time": "2020-06-16T01:34:07.603697Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:6245: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "# 用每列的均值填充其缺失值\n",
    "for i in Max.columns:\n",
    "    Max[i].fillna(value=Max[i].mean(),inplace=True)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:10.245846Z",
     "start_time": "2020-06-16T01:34:10.235840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Present_Tmax        0\n",
       "LDAPS_RHmax         0\n",
       "LDAPS_Tmax_lapse    0\n",
       "LDAPS_WS            0\n",
       "LDAPS_LH            0\n",
       "LDAPS_CC1           0\n",
       "LDAPS_CC2           0\n",
       "LDAPS_CC3           0\n",
       "LDAPS_CC4           0\n",
       "LDAPS_PPT1          0\n",
       "LDAPS_PPT4          0\n",
       "lat                 0\n",
       "lon                 0\n",
       "DEM                 0\n",
       "Slope               0\n",
       "Solar radiation     0\n",
       "Next_Tmax           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Max.isnull().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标准化处理连续型特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:15:24.140777Z",
     "start_time": "2020-06-16T02:15:24.099800Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "      <td>-0.376282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "      <td>0.072097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "      <td>0.264260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "      <td>0.456422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "      <td>0.296287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  -0.376282  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950   0.072097  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534   0.264260  \n",
       "3  0.385280 -0.297588  0.932424         1.201176   0.456422  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205   0.296287  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler \n",
    "ss = StandardScaler() # 按均值(μ)中⼼化后，再按标准差(σ)缩放，数据就变成满⾜均值为0，⽅差为1的分布\n",
    "\n",
    "Scaled_Data = ss.fit_transform(Max) \n",
    "Scaled_Data=pd.DataFrame(Scaled_Data,columns=Max.columns) \n",
    "Scaled_Data.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分特征列和标签列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:34:50.260685Z",
     "start_time": "2020-06-16T01:34:50.234704Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
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       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
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       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.30575</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  LDAPS_CC1  \\\n",
       "0     -0.361326     0.383078         -0.524889 -0.128382  0.206966  -0.516243   \n",
       "1      0.721084     0.311586          0.080895 -0.646994 -0.314841  -0.548557   \n",
       "2      0.619608    -0.614982          0.162936 -0.441604 -1.249283  -0.610450   \n",
       "3      0.754909     1.133054          0.031092 -0.666247  0.095997  -0.583539   \n",
       "4      0.551957     0.248765         -0.170325 -0.627154  1.354409  -0.832287   \n",
       "\n",
       "   LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4       lat  \\\n",
       "0  -0.592636  -0.629013  -0.664815    -0.30575   -0.224453  1.189286   \n",
       "1  -0.406199  -0.638055  -0.677462    -0.30575   -0.224453  1.189286   \n",
       "2  -0.384009  -0.458843  -0.620575    -0.30575   -0.224453  0.653021   \n",
       "3  -0.506548  -0.631178  -0.651696    -0.30575   -0.224453  1.991696   \n",
       "4  -0.413115  -0.559990  -0.510358    -0.30575   -0.224453  0.118743   \n",
       "\n",
       "        lon       DEM     Slope  Solar radiation  \n",
       "0 -0.005000  2.772243  1.115004         1.517935  \n",
       "1  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3  0.385280 -0.297588  0.932424         1.201176  \n",
       "4  1.807917 -0.494322 -0.548433         1.207205  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Scaled_Data.drop(['Next_Tmax'],axis=1) \n",
    "X.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:09.014361Z",
     "start_time": "2020-06-16T01:35:09.005367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7750   -4.123453\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7752, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = Scaled_Data['Next_Tmax']\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建线性回归模型并评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 划分数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:15.723572Z",
     "start_time": "2020-06-16T01:35:15.714578Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(X,Y,test_size=0.2,random_state=420)   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:18.264310Z",
     "start_time": "2020-06-16T01:35:18.247275Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.linear_model import LinearRegression,RidgeCV,LassoCV,SGDRegressor\n",
    "\n",
    "lr=LinearRegression().fit(x_train,y_train) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:32:40.607647Z",
     "start_time": "2020-06-15T06:32:40.603648Z"
    }
   },
   "source": [
    "## 评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:23.925342Z",
     "start_time": "2020-06-16T01:35:23.911350Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7570395775105323, 0.7428694968905715)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "lr.score(x_train,y_train),lr.score(x_test,y_test) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:24.025817Z",
     "start_time": "2020-06-16T01:22:24.016823Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.25266554606897074\n",
      "RMSE: 0.502658478560713\n"
     ]
    }
   ],
   "source": [
    "y_pred = lr.predict(x_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "MSE = metrics.mean_squared_error(y_test, y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(y_test, y_pred)) \n",
    "\n",
    "print('MSE:',MSE)\n",
    "print('RMSE:',RMSE) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型诊断"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制线性拟合图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:24.480812Z",
     "start_time": "2020-06-16T00:15:24.344885Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n从上图中可以看到RMSE(越小越好）的值0.5，我们通过可视化来看一下训练后的预测和真实值之间的差异：\\n'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "从上图中可以看到RMSE(越小越好）的值0.5，我们通过可视化来看一下训练后的预测和真实值之间的差异：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:30.193284Z",
     "start_time": "2020-06-16T01:22:29.785111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14795b973c8>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14795b8dd88>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x147982e2988>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画个折线图来看看模型拟合的好坏程度\n",
    "plt.figure(figsize=(20,10)) \n",
    "plt.plot(range(len(y_test)), y_test, 'r', label='Y-TEST')\n",
    "plt.plot(range(len(y_test)), y_pred, 'b', label='Y-PREDICTED')\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.123437Z",
     "start_time": "2020-06-16T00:15:25.118441Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到许多地方，红线会明显超出蓝线，说明我们的模型拟合度不够，再换个散点图来更直观地看一下：\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:22:33.969981Z",
     "start_time": "2020-06-16T01:22:33.602723Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14798321a48>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x14798760b08>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Y-TEST')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y-PREDICTED')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_test, y_pred)\n",
    "plt.plot([y_test.min(),y_test.max()], [y_test.min(),y_test.max()], 'k--') #构建一条Y的观测值=预测值的直线（理想状态）\n",
    "plt.xlabel('Y-TEST')\n",
    "plt.ylabel('Y-PREDICTED') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:25.593168Z",
     "start_time": "2020-06-16T00:15:25.588171Z"
    }
   },
   "source": [
    "'''\n",
    "图中我们可以看到，如果完全拟合，散点应该和直线相重合，这里发现，y_test有一些的异常值，\n",
    "而线性回归模型的一大缺点就是对异常值很敏感，会极大影响模型的准确性，因此，下一步，我们就根据这一点，对模型进行优化\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制残差图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:26.237802Z",
     "start_time": "2020-06-16T00:15:25.595167Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1d380c8fc48>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1d380c9a748>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Y_observed')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Y_predicted')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 也可以绘制残差图查看模型的拟合情况\n",
    "from sklearn.model_selection import cross_val_predict,cross_val_score\n",
    "\n",
    "Y_pred=cross_val_predict(lr,X,Y,cv=10)\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(20,10)) \n",
    "ax.scatter(Y,Y-Y_pred) \n",
    "ax.axhline(lw=8,color='blue')\n",
    "ax.set_xlabel('Y_observed')\n",
    "ax.set_ylabel('Y_predicted')\n",
    "plt.show()  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制相关性热图\n",
    "\n",
    "**绘制各预测变量间的相关性热图，查看是否存在相关性很高的变量（共线性或近似共线性）**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.379205Z",
     "start_time": "2020-06-16T00:15:26.240804Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d380c47d08>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMax=Scaled_Data.corr()   # 经过测试，使用标准化前后的数据得到的相关系数是一样的\n",
    "\n",
    "fig,ax=plt.subplots(figsize=(15,10))\n",
    "mask=np.array(corrMax)\n",
    "mask[np.tril_indices_from(mask)]=False\n",
    "sns.heatmap(corrMax,mask=mask, vmax=1,square=True,annot=True) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T01:35:34.870734Z",
     "start_time": "2020-06-16T01:35:34.674966Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1479fe7df88>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#####================寻找异常值====================######\n",
    "\n",
    "Scaled_Data['Next_Tmax'].plot.box() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:28:18.424085Z",
     "start_time": "2020-06-16T02:28:18.411090Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.662770694023558"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "-3.8575878207889565"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除掉Next_Tmax中小于下边缘值的记录\n",
    "Q1,Q3=np.percentile(Scaled_Data['Next_Tmax'],(0.25,0.75) )\n",
    "QD=Q3-Q1\n",
    "up_whisker=Q3+1.5*QD\n",
    "low_whisker=Q1-1.5*QD \n",
    "up_whisker\n",
    "low_whisker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:16:00.616309Z",
     "start_time": "2020-06-16T02:16:00.579332Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "      <th>Next_Tmax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>-2.695272</td>\n",
       "      <td>1.457979</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>4.911091</td>\n",
       "      <td>-0.210420</td>\n",
       "      <td>1.489658</td>\n",
       "      <td>2.281600</td>\n",
       "      <td>2.656311</td>\n",
       "      <td>2.268567</td>\n",
       "      <td>-0.017489</td>\n",
       "      <td>1.340116</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>-1.786106</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7750</th>\n",
       "      <td>-3.304127</td>\n",
       "      <td>-4.113443</td>\n",
       "      <td>-4.087857</td>\n",
       "      <td>-1.939757</td>\n",
       "      <td>-2.267499</td>\n",
       "      <td>-1.412018</td>\n",
       "      <td>-1.386643</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-1.758184</td>\n",
       "      <td>-2.082302</td>\n",
       "      <td>-0.911963</td>\n",
       "      <td>-0.845455</td>\n",
       "      <td>-2.358212</td>\n",
       "      <td>-4.123453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "6175     -2.695272     1.457979         -4.087857  4.911091 -0.210420   \n",
       "7750     -3.304127    -4.113443         -4.087857 -1.939757 -2.267499   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "6175   1.489658   2.281600   2.656311   2.268567   -0.017489    1.340116   \n",
       "7750  -1.412018  -1.386643  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  Next_Tmax  \n",
       "6175  1.189286 -0.005000  2.772243  1.115004        -1.786106  -4.123453  \n",
       "7750 -1.758184 -2.082302 -0.911963 -0.845455        -2.358212  -4.123453  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Y中小于箱型图下边缘的值\n",
    "Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:24:40.455480Z",
     "start_time": "2020-06-16T02:24:40.448484Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6175, 7750], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要删除的样本的Index\n",
    "drop_index=Scaled_Data[Scaled_Data['Next_Tmax']<low_whisker].index.values\n",
    "drop_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:42.036217Z",
     "start_time": "2020-06-16T02:25:41.961262Z"
    }
   },
   "outputs": [],
   "source": [
    "# 删除Y中小于箱型图下边缘的值\n",
    "X=X.drop(drop_index)\n",
    "Y=Y.drop(drop_index) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T02:25:54.069979Z",
     "start_time": "2020-06-16T02:25:54.035982Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7750 rows x 16 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
       "3       0.456422\n",
       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7750, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 删除后的X,Y -------少了两行\n",
    "X\n",
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:21:08.371115Z",
     "start_time": "2020-06-16T03:21:08.337137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.22952887192574323\n",
      "RMSE: 0.4790917155678474\n"
     ]
    }
   ],
   "source": [
    "# 重新划分数据集\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1)\n",
    "# 对训练集进行训练\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train, Y_train) \n",
    "# 对测试集进行预测\n",
    "Y_pred = lr.predict(X_test)\n",
    "MSE = metrics.mean_squared_error(Y_test, Y_pred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(Y_test, Y_pred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.726012Z",
     "start_time": "2020-06-16T00:15:27.652048Z"
    }
   },
   "outputs": [],
   "source": [
    "# 上一次评估结果\n",
    "# MSE: 0.25266554606897074\n",
    "# RMSE: 0.502658478560713  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:15:27.917423Z",
     "start_time": "2020-06-16T00:15:27.728011Z"
    }
   },
   "source": [
    "'''\n",
    "可以看到，该模型的RMSE由原来的0.5降低到了0.48，小幅度的提升了模型的准确率;去除异常值，提高准确度。\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:27.527723Z",
     "start_time": "2020-06-16T03:22:27.496745Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Present_Tmax</th>\n",
       "      <th>LDAPS_RHmax</th>\n",
       "      <th>LDAPS_Tmax_lapse</th>\n",
       "      <th>LDAPS_WS</th>\n",
       "      <th>LDAPS_LH</th>\n",
       "      <th>LDAPS_CC1</th>\n",
       "      <th>LDAPS_CC2</th>\n",
       "      <th>LDAPS_CC3</th>\n",
       "      <th>LDAPS_CC4</th>\n",
       "      <th>LDAPS_PPT1</th>\n",
       "      <th>LDAPS_PPT4</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>DEM</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Solar radiation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.361326</td>\n",
       "      <td>0.383078</td>\n",
       "      <td>-0.524889</td>\n",
       "      <td>-0.128382</td>\n",
       "      <td>0.206966</td>\n",
       "      <td>-0.516243</td>\n",
       "      <td>-0.592636</td>\n",
       "      <td>-0.629013</td>\n",
       "      <td>-0.664815</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>-0.005000</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>1.115004</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.721084</td>\n",
       "      <td>0.311586</td>\n",
       "      <td>0.080895</td>\n",
       "      <td>-0.646994</td>\n",
       "      <td>-0.314841</td>\n",
       "      <td>-0.548557</td>\n",
       "      <td>-0.406199</td>\n",
       "      <td>-0.638055</td>\n",
       "      <td>-0.677462</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.189286</td>\n",
       "      <td>0.511177</td>\n",
       "      <td>-0.315157</td>\n",
       "      <td>-0.542158</td>\n",
       "      <td>1.229950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.619608</td>\n",
       "      <td>-0.614982</td>\n",
       "      <td>0.162936</td>\n",
       "      <td>-0.441604</td>\n",
       "      <td>-1.249283</td>\n",
       "      <td>-0.610450</td>\n",
       "      <td>-0.384009</td>\n",
       "      <td>-0.458843</td>\n",
       "      <td>-0.620575</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.653021</td>\n",
       "      <td>0.838510</td>\n",
       "      <td>-0.526218</td>\n",
       "      <td>-0.723133</td>\n",
       "      <td>1.216534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.754909</td>\n",
       "      <td>1.133054</td>\n",
       "      <td>0.031092</td>\n",
       "      <td>-0.666247</td>\n",
       "      <td>0.095997</td>\n",
       "      <td>-0.583539</td>\n",
       "      <td>-0.506548</td>\n",
       "      <td>-0.631178</td>\n",
       "      <td>-0.651696</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>0.385280</td>\n",
       "      <td>-0.297588</td>\n",
       "      <td>0.932424</td>\n",
       "      <td>1.201176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.551957</td>\n",
       "      <td>0.248765</td>\n",
       "      <td>-0.170325</td>\n",
       "      <td>-0.627154</td>\n",
       "      <td>1.354409</td>\n",
       "      <td>-0.832287</td>\n",
       "      <td>-0.413115</td>\n",
       "      <td>-0.559990</td>\n",
       "      <td>-0.510358</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>0.118743</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>-0.494322</td>\n",
       "      <td>-0.548433</td>\n",
       "      <td>1.207205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-2.458495</td>\n",
       "      <td>-0.654605</td>\n",
       "      <td>-0.991760</td>\n",
       "      <td>-0.611932</td>\n",
       "      <td>0.585186</td>\n",
       "      <td>-1.157541</td>\n",
       "      <td>-1.291164</td>\n",
       "      <td>-1.278051</td>\n",
       "      <td>-1.112273</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.685654</td>\n",
       "      <td>1.191021</td>\n",
       "      <td>-0.735149</td>\n",
       "      <td>-0.820115</td>\n",
       "      <td>-2.096560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.328126</td>\n",
       "      <td>-1.112066</td>\n",
       "      <td>-0.436683</td>\n",
       "      <td>0.284622</td>\n",
       "      <td>-1.297018</td>\n",
       "      <td>-1.071078</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.149390</td>\n",
       "      <td>-1.263971</td>\n",
       "      <td>-0.852681</td>\n",
       "      <td>-0.803915</td>\n",
       "      <td>-2.093040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-2.187892</td>\n",
       "      <td>-1.548184</td>\n",
       "      <td>-0.887662</td>\n",
       "      <td>-0.255421</td>\n",
       "      <td>-0.454749</td>\n",
       "      <td>-1.274658</td>\n",
       "      <td>-1.094726</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.182118</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-1.037356</td>\n",
       "      <td>-0.821213</td>\n",
       "      <td>-0.755095</td>\n",
       "      <td>-2.104553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>-2.221718</td>\n",
       "      <td>-1.555342</td>\n",
       "      <td>-0.570780</td>\n",
       "      <td>0.088072</td>\n",
       "      <td>-1.591397</td>\n",
       "      <td>-1.224577</td>\n",
       "      <td>-1.153504</td>\n",
       "      <td>-1.278054</td>\n",
       "      <td>-1.178973</td>\n",
       "      <td>-0.305750</td>\n",
       "      <td>-0.224453</td>\n",
       "      <td>-0.417522</td>\n",
       "      <td>-0.269384</td>\n",
       "      <td>-0.779043</td>\n",
       "      <td>-0.719338</td>\n",
       "      <td>-2.074325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7751</th>\n",
       "      <td>2.649126</td>\n",
       "      <td>1.624409</td>\n",
       "      <td>3.044561</td>\n",
       "      <td>6.792009</td>\n",
       "      <td>4.496044</td>\n",
       "      <td>2.291644</td>\n",
       "      <td>2.384303</td>\n",
       "      <td>2.670813</td>\n",
       "      <td>2.669002</td>\n",
       "      <td>11.935477</td>\n",
       "      <td>13.651790</td>\n",
       "      <td>1.991696</td>\n",
       "      <td>1.807917</td>\n",
       "      <td>2.772243</td>\n",
       "      <td>2.861435</td>\n",
       "      <td>1.517935</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7750 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Present_Tmax  LDAPS_RHmax  LDAPS_Tmax_lapse  LDAPS_WS  LDAPS_LH  \\\n",
       "0        -0.361326     0.383078         -0.524889 -0.128382  0.206966   \n",
       "1         0.721084     0.311586          0.080895 -0.646994 -0.314841   \n",
       "2         0.619608    -0.614982          0.162936 -0.441604 -1.249283   \n",
       "3         0.754909     1.133054          0.031092 -0.666247  0.095997   \n",
       "4         0.551957     0.248765         -0.170325 -0.627154  1.354409   \n",
       "...            ...          ...               ...       ...       ...   \n",
       "7746     -2.458495    -0.654605         -0.991760 -0.611932  0.585186   \n",
       "7747     -2.187892    -1.328126         -1.112066 -0.436683  0.284622   \n",
       "7748     -2.187892    -1.548184         -0.887662 -0.255421 -0.454749   \n",
       "7749     -2.221718    -1.555342         -0.570780  0.088072 -1.591397   \n",
       "7751      2.649126     1.624409          3.044561  6.792009  4.496044   \n",
       "\n",
       "      LDAPS_CC1  LDAPS_CC2  LDAPS_CC3  LDAPS_CC4  LDAPS_PPT1  LDAPS_PPT4  \\\n",
       "0     -0.516243  -0.592636  -0.629013  -0.664815   -0.305750   -0.224453   \n",
       "1     -0.548557  -0.406199  -0.638055  -0.677462   -0.305750   -0.224453   \n",
       "2     -0.610450  -0.384009  -0.458843  -0.620575   -0.305750   -0.224453   \n",
       "3     -0.583539  -0.506548  -0.631178  -0.651696   -0.305750   -0.224453   \n",
       "4     -0.832287  -0.413115  -0.559990  -0.510358   -0.305750   -0.224453   \n",
       "...         ...        ...        ...        ...         ...         ...   \n",
       "7746  -1.157541  -1.291164  -1.278051  -1.112273   -0.305750   -0.224453   \n",
       "7747  -1.297018  -1.071078  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7748  -1.274658  -1.094726  -1.278054  -1.182118   -0.305750   -0.224453   \n",
       "7749  -1.224577  -1.153504  -1.278054  -1.178973   -0.305750   -0.224453   \n",
       "7751   2.291644   2.384303   2.670813   2.669002   11.935477   13.651790   \n",
       "\n",
       "           lat       lon       DEM     Slope  Solar radiation  \n",
       "0     1.189286 -0.005000  2.772243  1.115004         1.517935  \n",
       "1     1.189286  0.511177 -0.315157 -0.542158         1.229950  \n",
       "2     0.653021  0.838510 -0.526218 -0.723133         1.216534  \n",
       "3     1.991696  0.385280 -0.297588  0.932424         1.201176  \n",
       "4     0.118743  1.807917 -0.494322 -0.548433         1.207205  \n",
       "...        ...       ...       ...       ...              ...  \n",
       "7746 -0.685654  1.191021 -0.735149 -0.820115        -2.096560  \n",
       "7747 -0.149390 -1.263971 -0.852681 -0.803915        -2.093040  \n",
       "7748 -0.417522 -1.037356 -0.821213 -0.755095        -2.104553  \n",
       "7749 -0.417522 -0.269384 -0.779043 -0.719338        -2.074325  \n",
       "7751  1.991696  1.807917  2.772243  2.861435         1.517935  \n",
       "\n",
       "[7750 rows x 16 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:30.962802Z",
     "start_time": "2020-06-16T03:22:30.906836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征筛选，选出和样本标签之间存在显著相关性（p<0.05)的特征，从而过滤掉对模型预测贡献不大的特征\n",
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "F,p=f_regression(X,Y)  # 一个特征对应一个F值和p值\n",
    "k=F.shape[0] - (p>0.05).sum() # 去掉p值<=0.05的特征，k就是剩下的特征数量\n",
    "k "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:33.930084Z",
     "start_time": "2020-06-16T03:22:33.914094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7750, 14)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.36132577,  0.38307796, -0.52488856, ...,  1.18928568,\n",
       "         2.77224286,  1.11500407],\n",
       "       [ 0.72108401,  0.31158619,  0.08089519, ...,  1.18928568,\n",
       "        -0.31515742, -0.54215762],\n",
       "       [ 0.61960809, -0.61498177,  0.16293647, ...,  0.65302103,\n",
       "        -0.52621832, -0.7231326 ],\n",
       "       ...,\n",
       "       [-2.18789227, -1.54818379, -0.88766178, ..., -0.41752209,\n",
       "        -0.82121274, -0.75509512],\n",
       "       [-2.22171758, -1.55534234, -0.57077984, ..., -0.41752209,\n",
       "        -0.77904331, -0.71933797],\n",
       "       [ 2.64912642,  1.62440928,  3.04456067, ...,  1.99169648,\n",
       "         2.77224286,  2.86143459]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# F检验筛选特征\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "X_f=SelectKBest(f_regression,k=k).fit_transform(X,Y) \n",
    "X_f.shape    # 过滤掉了两个特征\n",
    "X_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:38.343703Z",
     "start_time": "2020-06-16T03:22:38.336692Z"
    }
   },
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(X_f,Y,test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:40.510995Z",
     "start_time": "2020-06-16T03:22:40.504012Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(xtrain,ytrain) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:45.574369Z",
     "start_time": "2020-06-16T03:22:45.562377Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7455182172970509, 0.759486375370554)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试集上的评分（R²）\n",
    "lr.score(xtrain,ytrain),lr.score(xtest,ytest) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:50.126024Z",
     "start_time": "2020-06-16T03:22:50.117031Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 0.23443534187388376\n",
      "RMSE: 0.4841852350845529\n"
     ]
    }
   ],
   "source": [
    "ypred = lr.predict(xtest) \n",
    "MSE = metrics.mean_squared_error(ytest, ypred)\n",
    "RMSE = np.sqrt(metrics.mean_squared_error(ytest, ypred)) \n",
    "print('MSE:',MSE) \n",
    "print('RMSE:',RMSE) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA降维\n",
    "\n",
    "主成分分析的目的是从现有的特征中重建新的特征，新的特征剔除了原有特征中的冗余信息，因此更有区分度。\n",
    "\n",
    "主成分分析的结果是得到新的特征，而不是简单地舍弃原来的特征列表中的一些特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:22:58.610715Z",
     "start_time": "2020-06-16T03:22:58.587725Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca=PCA(n_components=0.97).fit(X,Y) \n",
    "X_pca=pca.transform(X)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:04.873414Z",
     "start_time": "2020-06-16T03:23:04.832436Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
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       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.216577</td>\n",
       "      <td>3.103277</td>\n",
       "      <td>-0.110833</td>\n",
       "      <td>0.165686</td>\n",
       "      <td>0.790641</td>\n",
       "      <td>-0.681205</td>\n",
       "      <td>-1.317347</td>\n",
       "      <td>-0.238028</td>\n",
       "      <td>-0.203376</td>\n",
       "      <td>0.603971</td>\n",
       "      <td>-0.337200</td>\n",
       "      <td>0.358007</td>\n",
       "      <td>-0.423250</td>\n",
       "      <td>1.099815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.090162</td>\n",
       "      <td>-0.107501</td>\n",
       "      <td>1.262260</td>\n",
       "      <td>0.415918</td>\n",
       "      <td>0.528656</td>\n",
       "      <td>-0.648804</td>\n",
       "      <td>-1.089290</td>\n",
       "      <td>-0.077244</td>\n",
       "      <td>-0.327796</td>\n",
       "      <td>0.455522</td>\n",
       "      <td>0.200883</td>\n",
       "      <td>-0.248300</td>\n",
       "      <td>-0.819938</td>\n",
       "      <td>0.253945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.170936</td>\n",
       "      <td>-0.890398</td>\n",
       "      <td>0.501055</td>\n",
       "      <td>0.423675</td>\n",
       "      <td>0.775087</td>\n",
       "      <td>-1.372693</td>\n",
       "      <td>-0.972450</td>\n",
       "      <td>0.227718</td>\n",
       "      <td>0.223689</td>\n",
       "      <td>0.190134</td>\n",
       "      <td>0.099512</td>\n",
       "      <td>-0.201590</td>\n",
       "      <td>-0.922498</td>\n",
       "      <td>0.184477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.834243</td>\n",
       "      <td>1.217238</td>\n",
       "      <td>1.663132</td>\n",
       "      <td>0.726037</td>\n",
       "      <td>0.587636</td>\n",
       "      <td>-0.459220</td>\n",
       "      <td>-0.996600</td>\n",
       "      <td>-0.280367</td>\n",
       "      <td>-0.815869</td>\n",
       "      <td>0.995221</td>\n",
       "      <td>0.419671</td>\n",
       "      <td>-0.486277</td>\n",
       "      <td>-0.841180</td>\n",
       "      <td>-0.672248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.214938</td>\n",
       "      <td>0.125820</td>\n",
       "      <td>1.699884</td>\n",
       "      <td>0.871620</td>\n",
       "      <td>0.306261</td>\n",
       "      <td>0.069075</td>\n",
       "      <td>-0.711520</td>\n",
       "      <td>-0.439632</td>\n",
       "      <td>-0.165456</td>\n",
       "      <td>-1.432835</td>\n",
       "      <td>-0.681152</td>\n",
       "      <td>-0.127686</td>\n",
       "      <td>-0.932863</td>\n",
       "      <td>0.190224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7745</th>\n",
       "      <td>-1.786437</td>\n",
       "      <td>0.231800</td>\n",
       "      <td>-0.034922</td>\n",
       "      <td>0.032411</td>\n",
       "      <td>-3.623162</td>\n",
       "      <td>-0.618894</td>\n",
       "      <td>0.170518</td>\n",
       "      <td>0.229131</td>\n",
       "      <td>1.534717</td>\n",
       "      <td>-1.291132</td>\n",
       "      <td>-0.853182</td>\n",
       "      <td>-0.036252</td>\n",
       "      <td>-0.137269</td>\n",
       "      <td>-0.045560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7746</th>\n",
       "      <td>-1.871831</td>\n",
       "      <td>-0.153308</td>\n",
       "      <td>-1.028628</td>\n",
       "      <td>-0.810918</td>\n",
       "      <td>-3.635659</td>\n",
       "      <td>0.140778</td>\n",
       "      <td>-0.260850</td>\n",
       "      <td>0.833126</td>\n",
       "      <td>0.884765</td>\n",
       "      <td>0.511711</td>\n",
       "      <td>-0.727532</td>\n",
       "      <td>0.529375</td>\n",
       "      <td>-0.279357</td>\n",
       "      <td>-0.127452</td>\n",
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       "    <tr>\n",
       "      <th>7747</th>\n",
       "      <td>-1.904300</td>\n",
       "      <td>-0.383570</td>\n",
       "      <td>-1.415379</td>\n",
       "      <td>-0.856730</td>\n",
       "      <td>-3.348666</td>\n",
       "      <td>-0.376563</td>\n",
       "      <td>-0.161274</td>\n",
       "      <td>0.946401</td>\n",
       "      <td>1.242176</td>\n",
       "      <td>0.431955</td>\n",
       "      <td>-0.515940</td>\n",
       "      <td>0.329307</td>\n",
       "      <td>-0.214308</td>\n",
       "      <td>-0.169483</td>\n",
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       "    <tr>\n",
       "      <th>7748</th>\n",
       "      <td>-1.829787</td>\n",
       "      <td>-0.559760</td>\n",
       "      <td>-1.535874</td>\n",
       "      <td>-0.621879</td>\n",
       "      <td>-2.943078</td>\n",
       "      <td>-1.374214</td>\n",
       "      <td>-0.052400</td>\n",
       "      <td>1.204855</td>\n",
       "      <td>1.792964</td>\n",
       "      <td>0.294714</td>\n",
       "      <td>-0.109837</td>\n",
       "      <td>-0.169241</td>\n",
       "      <td>-0.115426</td>\n",
       "      <td>-0.188458</td>\n",
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       "    <tr>\n",
       "      <th>7749</th>\n",
       "      <td>8.725825</td>\n",
       "      <td>3.056087</td>\n",
       "      <td>4.609578</td>\n",
       "      <td>4.595430</td>\n",
       "      <td>8.330166</td>\n",
       "      <td>9.435757</td>\n",
       "      <td>5.962135</td>\n",
       "      <td>0.194339</td>\n",
       "      <td>10.953023</td>\n",
       "      <td>3.910947</td>\n",
       "      <td>0.153466</td>\n",
       "      <td>1.686319</td>\n",
       "      <td>-0.332968</td>\n",
       "      <td>0.823091</td>\n",
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       "<p>7750 rows × 14 columns</p>\n",
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       "             0         1         2         3         4         5         6  \\\n",
       "0    -0.216577  3.103277 -0.110833  0.165686  0.790641 -0.681205 -1.317347   \n",
       "1    -1.090162 -0.107501  1.262260  0.415918  0.528656 -0.648804 -1.089290   \n",
       "2    -1.170936 -0.890398  0.501055  0.423675  0.775087 -1.372693 -0.972450   \n",
       "3    -0.834243  1.217238  1.663132  0.726037  0.587636 -0.459220 -0.996600   \n",
       "4    -1.214938  0.125820  1.699884  0.871620  0.306261  0.069075 -0.711520   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "7745 -1.786437  0.231800 -0.034922  0.032411 -3.623162 -0.618894  0.170518   \n",
       "7746 -1.871831 -0.153308 -1.028628 -0.810918 -3.635659  0.140778 -0.260850   \n",
       "7747 -1.904300 -0.383570 -1.415379 -0.856730 -3.348666 -0.376563 -0.161274   \n",
       "7748 -1.829787 -0.559760 -1.535874 -0.621879 -2.943078 -1.374214 -0.052400   \n",
       "7749  8.725825  3.056087  4.609578  4.595430  8.330166  9.435757  5.962135   \n",
       "\n",
       "             7          8         9        10        11        12        13  \n",
       "0    -0.238028  -0.203376  0.603971 -0.337200  0.358007 -0.423250  1.099815  \n",
       "1    -0.077244  -0.327796  0.455522  0.200883 -0.248300 -0.819938  0.253945  \n",
       "2     0.227718   0.223689  0.190134  0.099512 -0.201590 -0.922498  0.184477  \n",
       "3    -0.280367  -0.815869  0.995221  0.419671 -0.486277 -0.841180 -0.672248  \n",
       "4    -0.439632  -0.165456 -1.432835 -0.681152 -0.127686 -0.932863  0.190224  \n",
       "...        ...        ...       ...       ...       ...       ...       ...  \n",
       "7745  0.229131   1.534717 -1.291132 -0.853182 -0.036252 -0.137269 -0.045560  \n",
       "7746  0.833126   0.884765  0.511711 -0.727532  0.529375 -0.279357 -0.127452  \n",
       "7747  0.946401   1.242176  0.431955 -0.515940  0.329307 -0.214308 -0.169483  \n",
       "7748  1.204855   1.792964  0.294714 -0.109837 -0.169241 -0.115426 -0.188458  \n",
       "7749  0.194339  10.953023  3.910947  0.153466  1.686319 -0.332968  0.823091  \n",
       "\n",
       "[7750 rows x 14 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pca=pd.DataFrame(X_pca)\n",
    "X_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:07.297511Z",
     "start_time": "2020-06-16T03:23:07.288516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      -0.376282\n",
       "1       0.072097\n",
       "2       0.264260\n",
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       "4       0.296287\n",
       "          ...   \n",
       "7746   -0.728580\n",
       "7747   -0.632499\n",
       "7748   -0.536418\n",
       "7749   -0.792634\n",
       "7751    2.762374\n",
       "Name: Next_Tmax, Length: 7750, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:10.305510Z",
     "start_time": "2020-06-16T03:23:10.277525Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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       "      <th>0</th>\n",
       "      <td>-0.228159</td>\n",
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       "      <td>0.069054</td>\n",
       "      <td>0.108524</td>\n",
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       "      <th>1</th>\n",
       "      <td>-0.177762</td>\n",
       "      <td>0.259298</td>\n",
       "      <td>-0.115402</td>\n",
       "      <td>0.139626</td>\n",
       "      <td>0.253892</td>\n",
       "      <td>-0.080516</td>\n",
       "      <td>-0.148532</td>\n",
       "      <td>-0.188108</td>\n",
       "      <td>-0.188080</td>\n",
       "      <td>0.000994</td>\n",
       "      <td>-0.060552</td>\n",
       "      <td>0.175085</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>0.573748</td>\n",
       "      <td>0.580675</td>\n",
       "      <td>0.008013</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.041856</td>\n",
       "      <td>0.375808</td>\n",
       "      <td>0.133046</td>\n",
       "      <td>-0.096357</td>\n",
       "      <td>0.346909</td>\n",
       "      <td>0.204690</td>\n",
       "      <td>0.055840</td>\n",
       "      <td>-0.139376</td>\n",
       "      <td>-0.192081</td>\n",
       "      <td>0.314158</td>\n",
       "      <td>-0.091826</td>\n",
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       "      <td>0.361806</td>\n",
       "      <td>-0.282789</td>\n",
       "      <td>-0.231722</td>\n",
       "      <td>0.160499</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.128714</td>\n",
       "      <td>-0.109038</td>\n",
       "      <td>-0.004835</td>\n",
       "      <td>-0.019345</td>\n",
       "      <td>0.086365</td>\n",
       "      <td>-0.269335</td>\n",
       "      <td>-0.083357</td>\n",
       "      <td>0.229271</td>\n",
       "      <td>0.328601</td>\n",
       "      <td>-0.366989</td>\n",
       "      <td>0.459058</td>\n",
       "      <td>0.392905</td>\n",
       "      <td>0.459881</td>\n",
       "      <td>0.010425</td>\n",
       "      <td>0.048139</td>\n",
       "      <td>-0.099735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.521499</td>\n",
       "      <td>-0.132052</td>\n",
       "      <td>0.415585</td>\n",
       "      <td>0.200768</td>\n",
       "      <td>-0.109847</td>\n",
       "      <td>0.064168</td>\n",
       "      <td>0.077508</td>\n",
       "      <td>0.086216</td>\n",
       "      <td>0.099563</td>\n",
       "      <td>0.174102</td>\n",
       "      <td>0.008157</td>\n",
       "      <td>-0.070494</td>\n",
       "      <td>0.084456</td>\n",
       "      <td>0.188751</td>\n",
       "      <td>0.208160</td>\n",
       "      <td>0.580179</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.234427</td>\n",
       "      <td>0.185108</td>\n",
       "      <td>-0.007308</td>\n",
       "      <td>0.228682</td>\n",
       "      <td>0.630832</td>\n",
       "      <td>-0.071018</td>\n",
       "      <td>-0.105731</td>\n",
       "      <td>-0.007234</td>\n",
       "      <td>0.102064</td>\n",
       "      <td>0.046327</td>\n",
       "      <td>0.409067</td>\n",
       "      <td>-0.213486</td>\n",
       "      <td>-0.445201</td>\n",
       "      <td>-0.111096</td>\n",
       "      <td>-0.084715</td>\n",
       "      <td>-0.031756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.377125</td>\n",
       "      <td>-0.085739</td>\n",
       "      <td>0.078499</td>\n",
       "      <td>0.002232</td>\n",
       "      <td>0.043436</td>\n",
       "      <td>0.071895</td>\n",
       "      <td>0.110212</td>\n",
       "      <td>0.108762</td>\n",
       "      <td>0.049060</td>\n",
       "      <td>0.444529</td>\n",
       "      <td>-0.093587</td>\n",
       "      <td>-0.110817</td>\n",
       "      <td>0.207857</td>\n",
       "      <td>0.087366</td>\n",
       "      <td>0.163401</td>\n",
       "      <td>-0.717383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.013714</td>\n",
       "      <td>-0.274986</td>\n",
       "      <td>-0.115457</td>\n",
       "      <td>0.835690</td>\n",
       "      <td>-0.034486</td>\n",
       "      <td>-0.002497</td>\n",
       "      <td>-0.048047</td>\n",
       "      <td>-0.071086</td>\n",
       "      <td>-0.092055</td>\n",
       "      <td>-0.053476</td>\n",
       "      <td>-0.223025</td>\n",
       "      <td>0.311199</td>\n",
       "      <td>-0.028485</td>\n",
       "      <td>-0.119224</td>\n",
       "      <td>-0.142026</td>\n",
       "      <td>-0.103987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.370291</td>\n",
       "      <td>-0.141150</td>\n",
       "      <td>0.121759</td>\n",
       "      <td>0.177218</td>\n",
       "      <td>-0.215333</td>\n",
       "      <td>-0.003833</td>\n",
       "      <td>-0.205263</td>\n",
       "      <td>-0.251031</td>\n",
       "      <td>-0.129045</td>\n",
       "      <td>0.434367</td>\n",
       "      <td>0.584006</td>\n",
       "      <td>-0.194281</td>\n",
       "      <td>0.238188</td>\n",
       "      <td>0.003956</td>\n",
       "      <td>-0.061626</td>\n",
       "      <td>0.022683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.042747</td>\n",
       "      <td>-0.017327</td>\n",
       "      <td>0.098680</td>\n",
       "      <td>-0.178946</td>\n",
       "      <td>-0.295307</td>\n",
       "      <td>-0.123886</td>\n",
       "      <td>-0.067586</td>\n",
       "      <td>0.010266</td>\n",
       "      <td>0.076458</td>\n",
       "      <td>0.310972</td>\n",
       "      <td>0.161651</td>\n",
       "      <td>0.616572</td>\n",
       "      <td>-0.574300</td>\n",
       "      <td>0.036589</td>\n",
       "      <td>0.062666</td>\n",
       "      <td>-0.070417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.228455</td>\n",
       "      <td>0.371271</td>\n",
       "      <td>0.185369</td>\n",
       "      <td>0.123466</td>\n",
       "      <td>-0.328695</td>\n",
       "      <td>0.234673</td>\n",
       "      <td>0.281510</td>\n",
       "      <td>-0.140353</td>\n",
       "      <td>-0.417383</td>\n",
       "      <td>-0.404841</td>\n",
       "      <td>0.328316</td>\n",
       "      <td>0.009849</td>\n",
       "      <td>-0.060252</td>\n",
       "      <td>-0.034421</td>\n",
       "      <td>0.054214</td>\n",
       "      <td>-0.211079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-0.034273</td>\n",
       "      <td>-0.637297</td>\n",
       "      <td>-0.063050</td>\n",
       "      <td>-0.208273</td>\n",
       "      <td>0.323071</td>\n",
       "      <td>0.465238</td>\n",
       "      <td>0.257615</td>\n",
       "      <td>-0.075044</td>\n",
       "      <td>-0.237589</td>\n",
       "      <td>-0.093466</td>\n",
       "      <td>0.184750</td>\n",
       "      <td>0.156335</td>\n",
       "      <td>-0.079597</td>\n",
       "      <td>0.142051</td>\n",
       "      <td>0.039583</td>\n",
       "      <td>0.025827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.352815</td>\n",
       "      <td>0.057000</td>\n",
       "      <td>0.671374</td>\n",
       "      <td>0.083665</td>\n",
       "      <td>0.102315</td>\n",
       "      <td>0.319565</td>\n",
       "      <td>-0.077398</td>\n",
       "      <td>-0.062434</td>\n",
       "      <td>0.416761</td>\n",
       "      <td>-0.181899</td>\n",
       "      <td>-0.123503</td>\n",
       "      <td>0.028882</td>\n",
       "      <td>-0.074246</td>\n",
       "      <td>0.140435</td>\n",
       "      <td>-0.075031</td>\n",
       "      <td>-0.195759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.048350</td>\n",
       "      <td>0.064477</td>\n",
       "      <td>0.013980</td>\n",
       "      <td>0.004915</td>\n",
       "      <td>0.015767</td>\n",
       "      <td>-0.187600</td>\n",
       "      <td>0.140001</td>\n",
       "      <td>0.123015</td>\n",
       "      <td>-0.138519</td>\n",
       "      <td>0.039964</td>\n",
       "      <td>0.012322</td>\n",
       "      <td>0.013607</td>\n",
       "      <td>0.012477</td>\n",
       "      <td>0.666108</td>\n",
       "      <td>-0.676660</td>\n",
       "      <td>-0.016238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6  \\\n",
       "0  -0.228159  0.259139 -0.363300  0.214156 -0.098169  0.389183  0.420652   \n",
       "1  -0.177762  0.259298 -0.115402  0.139626  0.253892 -0.080516 -0.148532   \n",
       "2   0.041856  0.375808  0.133046 -0.096357  0.346909  0.204690  0.055840   \n",
       "3   0.128714 -0.109038 -0.004835 -0.019345  0.086365 -0.269335 -0.083357   \n",
       "4   0.521499 -0.132052  0.415585  0.200768 -0.109847  0.064168  0.077508   \n",
       "5   0.234427  0.185108 -0.007308  0.228682  0.630832 -0.071018 -0.105731   \n",
       "6   0.377125 -0.085739  0.078499  0.002232  0.043436  0.071895  0.110212   \n",
       "7   0.013714 -0.274986 -0.115457  0.835690 -0.034486 -0.002497 -0.048047   \n",
       "8  -0.370291 -0.141150  0.121759  0.177218 -0.215333 -0.003833 -0.205263   \n",
       "9   0.042747 -0.017327  0.098680 -0.178946 -0.295307 -0.123886 -0.067586   \n",
       "10  0.228455  0.371271  0.185369  0.123466 -0.328695  0.234673  0.281510   \n",
       "11 -0.034273 -0.637297 -0.063050 -0.208273  0.323071  0.465238  0.257615   \n",
       "12 -0.352815  0.057000  0.671374  0.083665  0.102315  0.319565 -0.077398   \n",
       "13  0.048350  0.064477  0.013980  0.004915  0.015767 -0.187600  0.140001   \n",
       "\n",
       "           7         8         9        10        11        12        13  \\\n",
       "0   0.400378  0.343841  0.192053  0.162411  0.025265 -0.008065  0.076506   \n",
       "1  -0.188108 -0.188080  0.000994 -0.060552  0.175085  0.061905  0.573748   \n",
       "2  -0.139376 -0.192081  0.314158 -0.091826  0.459231  0.361806 -0.282789   \n",
       "3   0.229271  0.328601 -0.366989  0.459058  0.392905  0.459881  0.010425   \n",
       "4   0.086216  0.099563  0.174102  0.008157 -0.070494  0.084456  0.188751   \n",
       "5  -0.007234  0.102064  0.046327  0.409067 -0.213486 -0.445201 -0.111096   \n",
       "6   0.108762  0.049060  0.444529 -0.093587 -0.110817  0.207857  0.087366   \n",
       "7  -0.071086 -0.092055 -0.053476 -0.223025  0.311199 -0.028485 -0.119224   \n",
       "8  -0.251031 -0.129045  0.434367  0.584006 -0.194281  0.238188  0.003956   \n",
       "9   0.010266  0.076458  0.310972  0.161651  0.616572 -0.574300  0.036589   \n",
       "10 -0.140353 -0.417383 -0.404841  0.328316  0.009849 -0.060252 -0.034421   \n",
       "11 -0.075044 -0.237589 -0.093466  0.184750  0.156335 -0.079597  0.142051   \n",
       "12 -0.062434  0.416761 -0.181899 -0.123503  0.028882 -0.074246  0.140435   \n",
       "13  0.123015 -0.138519  0.039964  0.012322  0.013607  0.012477  0.666108   \n",
       "\n",
       "          14        15  \n",
       "0   0.069054  0.108524  \n",
       "1   0.580675  0.008013  \n",
       "2  -0.231722  0.160499  \n",
       "3   0.048139 -0.099735  \n",
       "4   0.208160  0.580179  \n",
       "5  -0.084715 -0.031756  \n",
       "6   0.163401 -0.717383  \n",
       "7  -0.142026 -0.103987  \n",
       "8  -0.061626  0.022683  \n",
       "9   0.062666 -0.070417  \n",
       "10  0.054214 -0.211079  \n",
       "11  0.039583  0.025827  \n",
       "12 -0.075031 -0.195759  \n",
       "13 -0.676660 -0.016238  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components=pca.components_   #14个新特征的特征向量(每一行) \n",
    "components=pd.DataFrame(components) \n",
    "components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:51.506451Z",
     "start_time": "2020-06-16T03:23:51.497454Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25542858, 0.12862968, 0.08850255, 0.08191585, 0.07267401,\n",
       "       0.06801911, 0.05608564, 0.05187105, 0.05015169, 0.04070604,\n",
       "       0.0339251 , 0.02304823, 0.01785588, 0.01306852])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_ #主成分的解释方差百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:54.442229Z",
     "start_time": "2020-06-16T03:23:54.432222Z"
    }
   },
   "outputs": [],
   "source": [
    "# 重新划分训练集和测试集\n",
    "Xtrain,Xtest,Ytrain,Ytest=train_test_split(X_pca,Y, test_size=0.3, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:57.857425Z",
     "start_time": "2020-06-16T03:23:57.848431Z"
    }
   },
   "outputs": [],
   "source": [
    "# # 实例化并训练模型\n",
    "lr=LinearRegression().fit(Xtrain,Ytrain) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T03:23:59.855182Z",
     "start_time": "2020-06-16T03:23:59.839193Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7383072663535957, 0.7383072663535957)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型在训练集和测试机上的评分（R²）\n",
    "lr.score(Xtrain,Ytrain),lr.score(Xtrain,Ytrain)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 随机森林回归 RandomForestRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:37.843504Z",
     "start_time": "2020-06-16T00:15:30.474184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=RandomForestRegressor(bootstrap=True, ccp_alpha=0.0,\n",
       "                                             criterion='mse', max_depth=None,\n",
       "                                             max_features='auto',\n",
       "                                             max_leaf_nodes=None,\n",
       "                                             max_samples=None,\n",
       "                                             min_impurity_decrease=0.0,\n",
       "                                             min_impurity_split=None,\n",
       "                                             min_samples_leaf=1,\n",
       "                                             min_samples_split=2,\n",
       "                                             min_weight_fraction_leaf=0.0,\n",
       "                                             n_estimators=100, n_jobs=None,\n",
       "                                             oob_score=False, random_state=None,\n",
       "                                             verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=1,\n",
       "             param_grid={'max_depth': range(1, 10),\n",
       "                         'n_estimators': range(1, 200, 10)},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用集成算法-随机森林回归器\n",
    "# 使用网格搜索寻找主要参数的最优取值组合\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "rfr=RandomForestRegressor()  # 默认criterion='mse' (但如果使用score接口，返回的是R²，并不是MSE) \n",
    "\n",
    "test_param={'n_estimators':range(1,200,10),   \n",
    "          'max_depth':range(1,10)}  \n",
    "\n",
    "GS=GridSearchCV(estimator=rfr,param_grid=test_param,n_jobs=1,cv=5)   \n",
    "\n",
    "GS.fit(X_train,Y_train)   # 使用去除异常值后的训练集数据训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:39:36.195735Z",
     "start_time": "2020-06-16T00:39:36.191739Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 9, 'n_estimators': 181}"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看最优参数组合\n",
    "GS.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:27.950412Z",
     "start_time": "2020-06-16T00:40:22.666442Z"
    }
   },
   "outputs": [],
   "source": [
    "# 用上面得到的最优参数组合重新实例化模型\n",
    "rfr=RandomForestRegressor(n_estimators=181,\n",
    "                         max_depth=9,).fit(X_train,Y_train)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:30.380572Z",
     "start_time": "2020-06-16T00:40:30.207691Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9150260289679837, 0.8561379054489956)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.score(X_train,Y_train),rfr.score(X_test,Y_test)   # 随机森林回归算法的score返回的是R²，并不是mse(尽管实例化时用的criterion='mse') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:40:48.947100Z",
     "start_time": "2020-06-16T00:40:39.499061Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1596168141063878"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # 使用交叉验证\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# 随机森林回归器在测试集上的MSE平均得分\n",
    "-cross_val_score(rfr,X_test,Y_test,cv=5, scoring='neg_mean_squared_error').mean()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-16T00:36:54.809803Z",
     "start_time": "2020-06-16T00:36:54.803807Z"
    },
    "scrolled": false
   },
   "source": [
    "'''\n",
    "可以看出：无论是在训练集上还是测试集上，随机森林的的表现均优于LinearRegression,Lasson和Ridge：\n",
    "训练集上的MSE由原来的0.25降到了0.15，R²由原来的0.75上升到了0.85。\n",
    "'''"
   ]
  }
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